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学者姓名：赵宜升
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Abstract ：
Aiming at the problem of low data rate in autonomous underwater vehicle (AUV)-based underwater acoustic communication system, a hybrid magnetic induction (MI) and reconfigurable intelligent surface (RIS)-assisted communication strategy is proposed to maximize system channel capacity. Specifically, an AUV first collects data from all the seafloor nodes by adopting the MI communication technology. Then, the AUV forwards the data to another AUV carried with a RIS by using the underwater acoustic communication method. With the help of the RIS, a strong reflective path between the first AUV and a surface base station (BS) on the sea is formed. The surface BS could receive the data at a relatively high data rate. In order to maximize the system channel capacity, the acoustic incidence angle, the distance between the first AUV and the RIS, the distance between the RIS and the surface BS, acoustic signal frequency, and transmitting power are jointly optimized. The formulated optimization problem is solved by employing a butterfly optimization algorithm (BOA) and an improved butterfly optimization algorithm (IBOA), respectively. Simulation results show that the IBOA can increase the system channel capacity more effectively than the basic BOA.
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| GB/T 7714 | Hu, Zhiyi , Zhao, Yisheng , Liu, Peng et al. Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System [J]. | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
| MLA | Hu, Zhiyi et al. "Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System" . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING (2024) . |
| APA | Hu, Zhiyi , Zhao, Yisheng , Liu, Peng , Song, Chaohua , Li, Tengteng . Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
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Traditional underwater wireless communication in single medium has the limitations of low rate and high delay. In this paper, magnetic induction (MI)-based cross-medium communication is taken into account to reduce the transmission delay. Specifically, multiple autonomous underwater vehicles are used to collect data from underwater sensor nodes by MI communication. The collected data is directly transferred to a unmanned aerial vehicle above the water via ultra-low frequency MI communication. The cross-medium data collection and transmission problem is formulated an optimization problem. The objective is to minimize the total delay under the constraints of transmitting power, transmission distance, and number of turns of MI coil. A standard particle swarm optimization (SPSO) algorithm and a quantum-behaved particle swarm optimization (QPSO) algorithm are adopted to obtain the suboptimal solution, respectively. Simulation results show that the QPSO algorithm is superior to the SPSO algorithm in reducing the total delay.
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| GB/T 7714 | Liu, Peng , Zhao, Yisheng , Hu, Zhiyi et al. MI-Based Cross-Medium Communication for Multi-AUV-Assisted Underwater Data Acquisition [J]. | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
| MLA | Liu, Peng et al. "MI-Based Cross-Medium Communication for Multi-AUV-Assisted Underwater Data Acquisition" . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING (2024) . |
| APA | Liu, Peng , Zhao, Yisheng , Hu, Zhiyi , Song, Chaohua , Li, Tengteng . MI-Based Cross-Medium Communication for Multi-AUV-Assisted Underwater Data Acquisition . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
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The traditional underwater wireless sensor network (UWSN) based on acoustic communication has the shortcomings of low data rate and limited battery power. In this paper, hybrid acoustic and magnetic induction (MI) communication are considered to overcome the above drawbacks. A resource allocation strategy in autonomous underwater vehicle (AUV)-assisted edge computing UWSN is investigated to minimize the total system delay. Specifically, all the sensor nodes (SNs) are divided into different clusters. The SNs within a cluster send the data to the cluster head (CH) via the acoustic communication. The CH forwards the data to the AUV by the MI communication. Then, the AUV moves to the position under a surface vehicle (SV) carried with a edge server. The AUV forwards the data to the edge server through the MI communication. The transmitting power, channel bandwidth, and computational resources are jointly optimized. The formulated non-convex optimization problem is solved by using an alternating iterative optimization algorithm. Compared with other schemes, the proposed strategy can reduce the total system delay more effectively.
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| GB/T 7714 | Li, Tengteng , Zhao, Yisheng , Hu, Zhiyi et al. Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication [J]. | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
| MLA | Li, Tengteng et al. "Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication" . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING (2024) . |
| APA | Li, Tengteng , Zhao, Yisheng , Hu, Zhiyi , Song, Chaohua , Liu, Peng . Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
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Aiming at the problem of limited energy stored in unmanned aerial vehicle (UAV), a resource allocation strategy for UAV-assisted edge computing in wireless powered communication networks is investigated in this paper. By deploying a laser beam director on the ground, sufficient energy can be provided for the UAV in a short period of time. Then, multiple ground terminals obtain energy from this UAV by radio frequency energy harvesting method and offload their computational tasks to the UAV with edge server. The resource allocation problem is modeled as an optimization problem. The optimization objective is to minimize the total energy consumption of the UAV subject to the constraints of energy and data causality, computational resources, and transmitting power. The suboptimal solution is obtained by introducing an imperialist competitive algorithm. Simulation results show that the imperialist competitive algorithm consumes less energy compared with the particle swarm optimization algorithm and the equal upload time allocation method.
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| GB/T 7714 | Zhang, Xinyu , Zhao, Yisheng , You, Hongyi et al. Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing [J]. | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
| MLA | Zhang, Xinyu et al. "Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing" . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING (2024) . |
| APA | Zhang, Xinyu , Zhao, Yisheng , You, Hongyi , Jian, Kaige , Liang, Li . Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing . | 2024 IEEE 99TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2024-SPRING , 2024 . |
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Abstract ：
The traditional underwater wireless sensor network (UWSN) based on acoustic communication has the shortcomings of low data rate and limited battery power. In this paper, hybrid acoustic and magnetic induction (MI) communication are considered to overcome the above drawbacks. A resource allocation strategy in autonomous underwater vehicle (AUV)-assisted edge computing UWSN is investigated to minimize the total system delay. Specifically, all the sensor nodes (SNs) are divided into different clusters. The SNs within a cluster send the data to the cluster head (CH) via the acoustic communication. The CH forwards the data to the AUV by the MI communication. Then, the AUV moves to the position under a surface vehicle (SV) carried with a edge server. The AUV forwards the data to the edge server through the MI communication. The transmitting power, channel bandwidth, and computational resources are jointly optimized. The formulated non-convex optimization problem is solved by using an alternating iterative optimization algorithm. Compared with other schemes, the proposed strategy can reduce the total system delay more effectively. © 2024 IEEE.
Keyword ：
Autonomous underwater vehicles Autonomous underwater vehicles Bandwidth Bandwidth Convex optimization Convex optimization Geophysical prospecting Geophysical prospecting Hybrid vehicles Hybrid vehicles Linear programming Linear programming Magnetic levitation vehicles Magnetic levitation vehicles Nonlinear programming Nonlinear programming Sensor nodes Sensor nodes
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| GB/T 7714 | Li, Tengteng , Zhao, Yisheng , Hu, Zhiyi et al. Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication [C] . 2024 . |
| MLA | Li, Tengteng et al. "Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication" . (2024) . |
| APA | Li, Tengteng , Zhao, Yisheng , Hu, Zhiyi , Song, Chaohua , Liu, Peng . Resource Allocation Strategy in AUV-Assisted Edge Computing UWSN with Hybrid Acoustic and MI Communication . (2024) . |
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Abstract ：
Aiming at the problem of limited energy stored in unmanned aerial vehicle (UAV), a resource allocation strategy for UAV-assisted edge computing in wireless powered communication networks is investigated in this paper. By deploying a laser beam director on the ground, sufficient energy can be provided for the UAV in a short period of time. Then, multiple ground terminals obtain energy from this UAV by radio frequency energy harvesting method and offload their computational tasks to the UAV with edge server. The resource allocation problem is modeled as an optimization problem. The optimization objective is to minimize the total energy consumption of the UAV subject to the constraints of energy and data causality, computational resources, and transmitting power. The suboptimal solution is obtained by introducing an imperialist competitive algorithm. Simulation results show that the imperialist competitive algorithm consumes less energy compared with the particle swarm optimization algorithm and the equal upload time allocation method. © 2024 IEEE.
Keyword ：
Aircraft communication Aircraft communication Edge computing Edge computing Energy utilization Energy utilization Particle swarm optimization (PSO) Particle swarm optimization (PSO) Resource allocation Resource allocation Unmanned aerial vehicles (UAV) Unmanned aerial vehicles (UAV)
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| GB/T 7714 | Zhang, Xinyu , Zhao, Yisheng , You, Hongyi et al. Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing [C] . 2024 . |
| MLA | Zhang, Xinyu et al. "Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing" . (2024) . |
| APA | Zhang, Xinyu , Zhao, Yisheng , You, Hongyi , Jian, Kaige , Liang, Li . Resource Allocation Strategy for Wireless Powered Communication Networks with UAV-Assisted Edge Computing . (2024) . |
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Abstract ：
Traditional underwater wireless communication in single medium has the limitations of low rate and high delay. In this paper, magnetic induction (MI)-based cross-medium communication is taken into account to reduce the transmission delay. Specifically, multiple autonomous underwater vehicles are used to collect data from underwater sensor nodes by MI communication. The collected data is directly transferred to a unmanned aerial vehicle above the water via ultra-low frequency MI communication. The cross-medium data collection and transmission problem is formulated an optimization problem. The objective is to minimize the total delay under the constraints of transmitting power, transmission distance, and number of turns of MI coil. A standard particle swarm optimization (SPSO) algorithm and a quantum-behaved particle swarm optimization (QPSO) algorithm are adopted to obtain the suboptimal solution, respectively. Simulation results show that the QPSO algorithm is superior to the SPSO algorithm in reducing the total delay. © 2024 IEEE.
Keyword ：
Aircraft communication Aircraft communication Autonomous underwater vehicles Autonomous underwater vehicles Inductive power transmission Inductive power transmission Magnetic levitation vehicles Magnetic levitation vehicles Particle swarm optimization (PSO) Particle swarm optimization (PSO) Sensor nodes Sensor nodes Unmanned aerial vehicles (UAV) Unmanned aerial vehicles (UAV)
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| GB/T 7714 | Liu, Peng , Zhao, Yisheng , Hu, Zhiyi et al. MI-Based Cross-Medium Communication for Multi-Auv-Assisted Underwater Data Acquisition [C] . 2024 . |
| MLA | Liu, Peng et al. "MI-Based Cross-Medium Communication for Multi-Auv-Assisted Underwater Data Acquisition" . (2024) . |
| APA | Liu, Peng , Zhao, Yisheng , Hu, Zhiyi , Song, Chaohua , Li, Tengteng . MI-Based Cross-Medium Communication for Multi-Auv-Assisted Underwater Data Acquisition . (2024) . |
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Abstract ：
Aiming at the problem of low data rate in autonomous underwater vehicle (AUV)-based underwater acoustic communication system, a hybrid magnetic induction (MI) and reconfigurable intelligent surface (RIS)-assisted communication strategy is proposed to maximize system channel capacity. Specifically, an AUV first collects data from all the seafloor nodes by adopting the MI communication technology. Then, the AUV forwards the data to another AUV carried with a RIS by using the underwater acoustic communication method. With the help of the RIS, a strong reflective path between the first AUV and a surface base station (BS) on the sea is formed. The surface BS could receive the data at a relatively high data rate. In order to maximize the system channel capacity, the acoustic incidence angle, the distance between the first AUV and the RIS, the distance between the RIS and the surface BS, acoustic signal frequency, and transmitting power are jointly optimized. The formulated optimization problem is solved by employing a butterfly optimization algorithm (BOA) and an improved butterfly optimization algorithm (IBOA), respectively. Simulation results show that the IBOA can increase the system channel capacity more effectively than the basic BOA. © 2024 IEEE.
Keyword ：
Data communication systems Data communication systems Interpolation Interpolation Linear programming Linear programming Magnetic levitation vehicles Magnetic levitation vehicles Underwater acoustics Underwater acoustics
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| GB/T 7714 | Hu, Zhiyi , Zhao, Yisheng , Liu, Peng et al. Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System [C] . 2024 . |
| MLA | Hu, Zhiyi et al. "Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System" . (2024) . |
| APA | Hu, Zhiyi , Zhao, Yisheng , Liu, Peng , Song, Chaohua , Li, Tengteng . Hybrid MI and RIS-Assisted Acoustic Communication for Channel Capacity Maximization in AUV-Based UWAC System . (2024) . |
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Abstract ：
针对边缘计算系统中多个计算任务之间存在某种依赖关系的特点,研究一种最小化总计算时间的资源分配策略.考虑多个任务之间的顺序依赖关系,用户的多个任务按顺序依次卸载;在当前任务卸载完成时,不用等该任务完成计算,就开始卸载下一个任务.通过引入一种两层卸载策略,用户可以先将任务卸载到小基站边缘服务器,当小基站边缘服务器计算能力不足时,小基站再将部分任务卸载到宏基站边缘服务器.建立联合优化用户关联、计算资源和用户发射功率的资源分配问题,达到最小化总计算时间的目标.采用量子行为粒子群优化算法进行求解,得到全局次优解.仿真结果表明,与标准粒子群优化算法和其他基准策略相比,使用量子行为粒子群优化算法所得到的总计算时间更少.
Keyword ：
多任务 多任务 资源分配 资源分配 边缘计算 边缘计算 量子行为粒子群优化 量子行为粒子群优化
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| GB/T 7714 | 陈勇 , 赵宜升 , 贺喜梅 et al. 多任务边缘计算中最小化时间的资源分配方法 [J]. | 杭州电子科技大学学报 , 2024 , 44 (9) : 1-8 . |
| MLA | 陈勇 et al. "多任务边缘计算中最小化时间的资源分配方法" . | 杭州电子科技大学学报 44 . 9 (2024) : 1-8 . |
| APA | 陈勇 , 赵宜升 , 贺喜梅 , 徐志红 . 多任务边缘计算中最小化时间的资源分配方法 . | 杭州电子科技大学学报 , 2024 , 44 (9) , 1-8 . |
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Abstract ：
Aiming at excessive users existing in a pico base station (PBS) in the multi-layer heterogeneous networks, the resource allocation problem of maximizing the energy efficiency of the networks is investigated in this paper. By deploying a relay node with energy harvesting function, the data of some users in the PBS can be transferred to an adjacent idle PBS. The bandwidth and transmitting power of users and the relay node are both considered to formulate the resource allocation optimization problem. The objective is to maximize the energy efficiency of the whole heterogeneous networks under the constraints of the user’s minimum data rate and energy consumption. The suboptimal solution is obtained by using the particle swarm optimization (PSO) algorithm and quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the adopted methods have higher energy efficiency than the conventional fixed power and bandwidth method. In addition, the time complexity of the adopted methods is relatively low. © 2021, Shanghai Jiao Tong University and Springer-Verlag GmbH Germany, part of Springer Nature.
Keyword ：
A A energy efficiency energy efficiency energy harvesting energy harvesting heterogeneous networks heterogeneous networks TN 915.65 TN 915.65
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| GB/T 7714 | Gao, J. , Zhao, Y. , Chen, J. et al. Energy-Efficient Bandwidth and Power Allocation in Relay-Assisted Multi-Layer Heterogeneous Networks with Energy Harvesting; [具有能量收集的中继辅助多层异构网络的能量高效带宽和功率分配策略] [J]. | Journal of Shanghai Jiaotong University (Science) , 2023 , 28 (6) : 822-830 . |
| MLA | Gao, J. et al. "Energy-Efficient Bandwidth and Power Allocation in Relay-Assisted Multi-Layer Heterogeneous Networks with Energy Harvesting; [具有能量收集的中继辅助多层异构网络的能量高效带宽和功率分配策略]" . | Journal of Shanghai Jiaotong University (Science) 28 . 6 (2023) : 822-830 . |
| APA | Gao, J. , Zhao, Y. , Chen, J. , Chen, Z. . Energy-Efficient Bandwidth and Power Allocation in Relay-Assisted Multi-Layer Heterogeneous Networks with Energy Harvesting; [具有能量收集的中继辅助多层异构网络的能量高效带宽和功率分配策略] . | Journal of Shanghai Jiaotong University (Science) , 2023 , 28 (6) , 822-830 . |
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